Performance measurements systems: Moving beyond the metric

Brian Backer
Tags: business management

You can’t manage what you don’t understand, and you can’t understand what you don’t measure. Heard this one before? Chances are you have, but how well do we really practice this simple philosophy?

If we can accept this simple adage to be true, then poor measurement leads to faulty insights and, ultimately, less-than-ideal corporate performance management. Conversely, good measurement leads to valuable insights and successful corporate performance management.

So, why do so many companies shortchange themselves when it comes to their performance measurement systems? With capital markets being as they are and pressure constantly mounting to demonstrate good measures of financial performance, more focus must be given to measurement systems to both explain and predict customer behavior, brand health and ultimately financial performance. In short, any successful corporate performance management system must be backed by organizational support for a strong corporate performance measurement system.

Notice the repeated use of the term “measurement system.” All successful companies have performance metrics by which they manage. Examples of such include everything from defect rates to time-in-transit to customer satisfaction scores and return on investment. But what is a measurement system? According to The American Heritage Dictionary, a system is:

A group of interacting, interrelated or interdependent elements forming a complex whole.

So, a true measurement system is not only a simple collection of performance metrics, but one that quantifies how these individual metrics are “interacting, interrelated or interdependent.” The latter highlights where many collections of metrics such as business scorecards fail. So, why is this system concept important in how we look at performance metrics?

Well, consider the fact that we place so much emphasis on metrics to quantify what happened. More often than not, the consequences of making a bad decision on such information are too high to leave to speculation, so we measure such things as defect rates, first call problem resolution, on-time delivery, etc. If we find it important enough to quantitatively measure what happened, we should find it just as important to understand why it happened.

Measurement systems that utilize statistical techniques to quantify how all of these metrics are “interacting, interrelated or interdependent” help quantitatively explain why a certain phenomenon we observe happened. If the consequences of leaving what happened to mere guesswork are too costly, why do companies so often leave the why to speculation? By taking our collection of metrics and building a model that quantitatively shows how they interrelate, we have the foundation of a system that helps better understand each facet of how our businesses operate.

Taking a step beyond this, if we are able to better explain how something occurred in the past, we’re better prepared to predict what may occur in the future. If we can quantitatively explain how Metric X impacts Metric Y, we can begin to understand how to spend budgets to provide maximum benefit, better utilizing each dollar spent. Measurement systems, as predictive models, help to prioritize improvement and innovation efforts, while facilitating contingency planning based on internal, industry, or macroeconomic conditions.

In short, better measurement leads to better insights leads to better decisions and better performance.

About the author:
Brian Backer is an experienced researcher and consultant in customer satisfaction, customer loyalty, and utilizing customer feedback programs to drive process improvement and market innovation initiatives. He has a strong background in satisfaction and loyalty measurement as well as in business intelligence and process improvement methodologies such as lean and Six Sigma. For more information, e-mail

New Call-to-action

About the Author